Senior Research Fellow, Applied Artificial Intelligence

Savannah River National Laboratory seeks an accomplished and visionary Senior Research Fellow in Applied Artificial Intelligence to report to the Associate Laboratory Director for Science, Energy and Innovation. This role will provide scientific leadership for the development and application of AI-enabled methods - including generative AI, digital twins, physics-informed machine learning, uncertainty-aware prediction, and advanced decision support - across SRNL's mission portfolio. The selected leader will help shape laboratory strategy, build a high-value applied AI portfolio, catalyze matrixed collaboration across directorates, and strengthen SRNL's position as a trusted partner for mission-critical innovation. The ideal candidate combines deep technical credibility with the ability to translate advanced analytics into operationally relevant, sponsor-valued outcomes.

Why this role matters at SRNL

SRNL's newly established Science, Energy and Innovation Directorate is intended to integrate fundamental research, energy resilience, and innovation under a unified framework. Applied AI can serve as a force multiplier across that portfolio when anchored in mission realities rather than treated as a stand-alone digital initiative.

* Develop and continuously refine SRNL's applied AI strategy and technical vision for the SEI directorate and enterprise partners, with clear alignment to mission demand, institutional capabilities, and sponsor priorities.

* Define the laboratory's technical point of view on where generative AI, physics-informed machine learning, digital twins, autonomous experimentation, knowledge systems, and decision-support analytics create the greatest mission advantage.

* Translate strategic intent into a sequenced portfolio of near-term wins, medium-term capability builds, and long-horizon differentiators.

Mission-integrated research and development leadership

* Serve as senior scientific lead for mission-facing AI initiatives affecting environmental stewardship, national security, energy resilience, nuclear materials, subsurface characterization, atmospheric modeling, and related domains.

* Identify high-value use cases where AI can improve throughput, quality, prediction, knowledge capture, safety margin, anomaly detection, maintenance planning, or scientific discovery.

* Ensure that AI efforts are grounded in domain science and engineering, not disconnected algorithm development.

* Support a matrixed operating model in which domain experts across directorates partner with a core AI capability to execute strategically selected projects.

* Establish expectations for validation, uncertainty quantification, verification, reproducibility, data quality, model documentation, and human-in-the-loop decision support appropriate for mission-critical applications.

* Advise laboratory leadership on technical guardrails for responsible AI deployment in high-consequence scientific and engineering environments.

* Lead or sponsor peer reviews and technical risk reviews for major AI-enabled projects and serve as a visible advocate for evidence-based adoption.

* Advise on the data curation, metadata, knowledge management, compute, software, and workflow architecture required to support a credible applied AI enterprise.

* Partner with research computing, information technology, cyber, and line organizations to ensure infrastructure decisions serve scientific use cases, security requirements, and scaling needs.

* Promote pragmatic integration of internal data resources, scientific software environments, GPU/HPC resources, and secure model deployment pathways.

External partnerships and laboratory representation

* Represent SRNL with DOE program offices, other national laboratories, universities, industry partners, and technical societies on matters related to applied AI and AI-enabled science and engineering.

* Develop strong collaborations that position SRNL as a preferred partner for AI-enabled applied research, especially where SRNL brings unique facilities, datasets, and mission context.

* Contribute to a growth strategy that converts technical credibility into sponsor confidence, multi-institution initiatives, and mission-relevant funding.

Mentoring, talent development, and culture building

* Mentor principal investigators, research staff, postdoctoral researchers, and early-career AI and domain scientists working at the intersection of mission science and advanced analytics.

* Help recruit and retain high-end technical talent by making SRNL an attractive destination for applied AI researchers who want to work on consequential real-world problems.

* Model a culture of collaboration, scientific integrity, disciplined execution, inclusion, and technical ambition.

Specific Role Expectations

Expected technical domains

* Generative AI and scientific knowledge systems, including retrieval-augmented workflows and technical knowledge capture.

* Physics-informed and hybrid AI/ML methods for complex physical, chemical, and engineered systems.

* Digital twins, surrogate models, and uncertainty-aware prediction for operations and decision support.

* Data-centric AI, curation pipelines, and scientific workflow integration for laboratory environments.

* Applied AI in high-consequence settings where validation, interpretability, provenance, and governance are essential.

* Lead through technical authority, strategic framing, and matrixed collaboration rather than relying primarily on formal line authority.

* Balance research creativity with sponsor relevance, operational realism, and institutional discipline.

First-order deliverables in the first 12 months

* A documented applied AI strategy and opportunity map for SRNL

* A prioritized pipeline of LDRD, proposal, and partnership opportunities with clear owners and maturity stages.

* A recommended governance framework for validation, model trust, data quality, and responsible deployment.

* A small number of flagship use cases or demonstration efforts that visibly establish momentum and credibility.

* Ph.D., 10 yr+ in computer science, applied mathematics, computational science, engineering, physics, chemistry, or a closely related technical discipline.

* Nationally recognized record of accomplishment in applied artificial intelligence, computational science, or AI-enabled scientific/engineering research, with visible technical contributions and external credibility.

* Demonstrated success translating advanced AI/ML methods into real research, engineering, operational, or decision-support outcomes rather than purely theoretical work.

* Strong understanding of scientific modeling, validation, uncertainty quantification, and the technical limitations of AI in complex physical systems.

* Evidence of building externally funded programs, major partnerships, or strategically important technical initiatives in national laboratories, government R&D organizations, universities, or advanced industry.

* Demonstrated ability to work effectively across disciplines and influence senior technical leaders, program managers, and executives.

* Ability to obtain and maintain a DOE Q clearance.

Preferred qualifications

* Experience in one or more SRNL-relevant application areas such as nuclear materials, radiochemistry, tritium systems, separations, environmental monitoring, subsurface science, atmospheric modeling, advanced materials, or high-consequence industrial operations.

* Experience with HPC/GPU computing environments, scientific data platforms, and secure deployment of AI tools in regulated or sensitive settings.

* Experience shaping or leading matrixed, or cross-organizational technical programs.

* Track record with DOE sponsors, especially EM, NNSA, Office of Science, ARPA-E, or other mission agencies relevant to SRNL growth.

* Experience mentoring technical staff and helping build distinguished research communities.

Leadership characteristics

* Scientifically credible and strategically ambitious, but disciplined about validation, deployment reality, and mission alignment.

* Comfortable operating across research, engineering, operations, computing, and sponsor interfaces.

* Builder of followership who can inspire top technical talent without overcentralizing authority.

* Clear communicator who can translate advanced AI ideas for executives, sponsors, operators, and domain scientists.

* Bias for action paired with intellectual honesty about technical risk, uncertainty, and readiness.

"We put science to work!"

Savannah River National Laboratory (SRNL) is a multi-program laboratory applying state of the art science and practical, high-value, cost-effective solutions to complex technical problems to protect the nation. Located at the U.S. Department of Energy's (DOE) Savannah River Site (SRS) in Aiken SC, the laboratory develops and deploys innovative technologies to address some of the nation's environmental, energy, and national security challenges.

Battelle Savannah River Alliance (BSRA) is constantly assessing trends to provide the best possible benefits to our workforce. We also negotiate cost effective premiums that will meet the needs of our evolving workforce.

Some of the *Benefits offered to employees include:

*Benefits vary based upon employment status

• Highly competitive Medical, Dental, and Vision options including HSA options with company provided seed
• Short- & Long-Term Disability (company paid)
• Life Insurance Non-Contributary 1X salary (company paid)
• AD&D Non-contributary 1x salary (company paid)
• Savings & Investment plan:
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  • Qualified Non-Elective Company Contribution of 5% each pay period with immediate vesting
  • Company match 50 cents/dollar up to 8% (5 yrs. vesting in company match)
  
  
• Contributory Life Insurance up to 5x Salary with $1M Cap
• Contributory AD&D (employee, spouse and children)
• Paid Time Off
• Employee Assistance Plan
• SRNL offers a competitive relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions.

For more information about our benefits, working here, and living here, visit the "About" tab at www.srnl.doe.gov.

BSRA is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected veteran status. BSRA is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. Please email us at SRNLRecruiting@srnl.doe.gov with any questions regarding the hiring process or to request an accommodation.